A Method for Constructing a Digital Transformation Driving Mechanism Based on Semantic Understanding of Large Models
This addresses digital transformation challenges for enterprises, particularly in manufacturing, by improving decision-making efficiency, but it is incremental as it builds on existing techniques like BERT, GPT-4, and GNNs.
The study tackled the problem of insufficient semantic understanding and lack of intelligent decision-making in digital transformation by proposing a method combining LLMs and knowledge graphs, which reduced equipment failure response time from 7.8 to 3.7 hours and decreased decision error compensation costs by 45.3%.
In the process of digital transformation, enterprises are faced with problems such as insufficient semantic understanding of unstructured data and lack of intelligent decision-making basis in driving mechanisms. This study proposes a method that combines a large language model (LLM) and a knowledge graph. First, a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model is used to perform entity recognition and relationship extraction on multi-source heterogeneous texts, and GPT-4 is used to generate semantically enhanced vector representations; secondly, a two-layer graph neural network (GNN) architecture is designed to fuse the semantic vectors output by LLM with business metadata to construct a dynamic and scalable enterprise knowledge graph; then reinforcement learning is introduced to optimize decision path generation, and the reward function is used to drive the mechanism iteration. In the case of the manufacturing industry, this mechanism reduced the response time for equipment failure scenarios from 7.8 hours to 3.7 hours, the F1 value reached 94.3%, and the compensation for decision errors in the annual digital transformation cost decreased by 45.3%. This method significantly enhances the intelligence level and execution efficiency of the digital transformation driving mechanism by integrating large model semantic understanding with structured knowledge.